Surgeon length of service and risk-adjusted outcomes: linked observational analysis of the UK National Adult Cardiac Surgery Audit Registry and General Medical Council Register

Machine learning algorithms to predict intraoperative hemorrhage in surgical patients: a modeling study of real-world data in Shanghai, China

BACKGROUND

Prediction tools for various intraoperative bleeding events remain scarce. We aim to develop machine learning-based models and identify the most important predictors by real-world data from electronic medical records (EMRs).

METHODS

An established database of surgical inpatients in Shanghai was utilized for analysis. A total of 51,173 inpatients were assessed for eligibility. 48,543 inpatients were obtained in the dataset and patients were divided into haemorrhage (N = 9728) and without-haemorrhage (N = 38,815) groups according to their bleeding during the procedure. Candidate predictors were selected from 27 variables, including sex (N = 48,543), age (N = 48,543), BMI (N = 48,543), renal disease (N = 26), heart disease (N = 1309), hypertension (N = 9579), diabetes (N = 4165), coagulopathy (N = 47), and other features. The models were constructed by 7 machine learning algorithms, i.e., light gradient boosting (LGB), extreme gradient boosting (XGB), cathepsin B (CatB), Ada-boosting of decision tree (AdaB), logistic regression (LR), long short-term memory (LSTM), and multilayer perception (MLP). An area under the receiver operating characteristic curve (AUC) was used to evaluate the model performance.

RESULTS

The mean age of the inpatients was 53 ± 17 years, and 57.5% were male. LGB showed the best predictive performance for intraoperative bleeding combining multiple indicators (AUC = 0.933, sensitivity = 0.87, specificity = 0.85, accuracy = 0.87) compared with XGB, CatB, AdaB, LR, MLP and LSTM. The three most important predictors identified by LGB were operative time, D-dimer (DD), and age.

CONCLUSIONS

We proposed LGB as the best Gradient Boosting Decision Tree (GBDT) algorithm for the evaluation of intraoperative bleeding. It is considered a simple and useful tool for predicting intraoperative bleeding in clinical settings. Operative time, DD, and age should receive attention.

The Management of Metastatic Spinal Cord Compression in Routine Clinical Practice

(1) Background: Whether clinical management of spinal metastatic disease (SMD) matches evidence-based recommendations is largely unknown. (2) Patients and Methods: A questionnaire was distributed through Spanish Medical Societies, exploring routine practice, interpretation of the SINS and ESCC scores and agreement with items in the Tokuhashi and SINS scales, and NICE guideline recommendations. Questionnaires were completed voluntarily and anonymously, without compensation. (3) Results: Eighty specialists participated in the study. A protocol for patients with SMD existed in 33.7% of the hospitals, a specific multidisciplinary board in 33.7%, 40% of radiological reports included the ESCC score, and a prognostic scoring method was used in 73.7%. While 77.5% of the participants were familiar with SINS, only 60% used it. The different SINS and ESCC scores were interpreted correctly by 57.5-70.0% and 30.0-37.5% of the participants, respectively. Over 70% agreed with the items included in the SINS and Tokuhashi scores and with the recommendations from the NICE guideline. Differences were found across private/public sectors, hospital complexity, number of years of experience, number of patients with SMD seen annually and especially across specialties. (4) Conclusions: Most specialists know and agree with features defining the gold standard treatment for patients with SCC, but many do not apply them.

Multicenter randomized trial exploring effects of simulation-based ultrasound training on obstetricians' diagnostic accuracy: value for experienced operators

OBJECTIVE

To explore the effects of simulation-based ultrasound training on the accuracy of fetal weight estimation in the third trimester among obstetricians with different levels of clinical experience.

METHODS

This was a multicenter, randomized pre-post-test practical trial conducted between March 2016 and January 2018. Obstetricians with different levels of clinical experience were randomized to either simulation-based ultrasound training focusing on fetal weight scans or no intervention. Participants completed two scans in pregnant women at term to establish baseline accuracy of fetal weight estimation. Another two scans were performed at follow-up. Accuracy was defined by the percentage difference between estimated fetal weight and actual birth weight. Ultrasound image quality was rated by two expert raters.

RESULTS

Seventy participants with different levels of clinical experience completed the study. Adjusting for clinical experience, the intervention group demonstrated an improvement in measurement accuracy of 31.9% (95% CI, 6.9-50.1%) (P = 0.02), whereas the control group did not improve (relative difference, 13.1% (95% CI, -17.9 to 55.9%); P = 0.45). The change in accuracy was significantly different between the groups (P = 0.02) and independent of clinical experience (P = 0.54). Image-quality scores improved by a mean of 1.2 (95% CI, 0.4-2.1) (P < 0.01) in the intervention group, with no change in the control group (mean difference, 0.1 (95% CI, -0.8 to 1.0); P = 0.78). There was a strong negative correlation between time spent using the simulator and clinical experience (r = -0.70, P = 0.0001).

CONCLUSION

Simulation-based ultrasound training improved accuracy and image quality when performing fetal weight estimation in women at term, independent of obstetricians' clinical experience. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.